Since the Treebank cannot provide sufficient amount of samples which cover all types of phrasal structures, it results that grammars directly extracted from treebanks suffer low coverage rate [5].

grammar generalization and specialization processes are carried out to obtain grammars with better coverage and precision.


Experimental results of the Sinica testing data showed that the grammar token coverage increased from 92.8% to 99.4%, but the labeling F-score dropped from 82.43% to 82.11% [7].

feature constraints were added into binary rules to limit over-generation caused by recursively adding constituents into intermediate-phrase types, such as S' at above example.


However the side-effect is that it also reduces grammar coverage.

grammar design is balanced between its precision and coverage.


Since all features have their own linguistic motivations, the result feature constrained grammars maintain high coverage and have improving grammar precision.

each feature more or less improves the parsing performance and the feature of leftmost daughter node, which constrains the linear order of constituents, is the most effective feature.


However such difference seems not very significant in Chinese.

feature selection and assignment should be linguistically-motivated as shown in our experiments.


-For prepositions (p): • Prepositions are converted into lower case.

21M (v, p, n 2 ) triples and 147M (n, p, n 2 ) triples,in total 168M triples, were acquired.


They are seldom used for other purposes than exclamation and question marks.

the focus of sentence identification is on period disambiguation to resolve the ambiguity of ".


Synonyms in MCTs are considered to have the same context since they generally share the same role.

agreement of surrounding context is a key feature for synonym extraction.


It is infeasible to run the ITG biparsing algorithm on n 2 candidate sentence pairs.

a multi-stage algorithm is needed that first generates likely candidates using faster heuristics, and then biparses the candidates to obtain the final high-precision results.


Therefore, rather than arbitrarily setting a threshold, we are interested in evaluation metrics that can show whether the ITG model is highly effective at any desired tradeoff points.

we assess the contribution of ITG ranking by computing standard uninterpolated average precision scores used to evaluate the effectiveness of ranking methods.


Our new score, F P P (W ), which is a combination of PP and TF, is defined as their product: F P P (W ) = tf (W )P P (W ) ⇒ log 2 F P P (W ) = log 2 tf (W ) + log 2 P P (W ) We avoided the problem of log 2 tf (W ) being undefined with tf (W ) = 0 5 by applying the adding-one smoothing to tf (W ).

the above definition of log F P P (W ) changed as follows: We used this log 2 F P P (W ) measure for evaluation.


Note that the data used in the preliminary experiments described in Section 3.3 are a part of Corpus A.

we can compare the results for humans with those for Corpus A in this experiment.


So to properly support analogy, a taxonomy must provide a basis of abstracting not just to conventional categories, like Person, Animal or Tool, but to categories representing the specific causal behaviour of concepts such as think-agent, pain-experiencer, cutting-instrument, and so on.

a surgeon can be meaningfully described as a repairman since both occupations have the function of restoring an object to an earlier and better state; a footballer can be meaningfully described as a gladiator or a warrior since each exhibits competitive behaviour; and a scalpel can be compared to a sabre, a sword or a cleaver since each has a cutting behaviour; and so on.


It is also difficult to build a domain-specific thesaurus flexibly.

it is necessary to construct thesauri automatically using computers.


Also, co-occurrence data extracted from corpora are often very sparse, making them inappropriate for similarity calculation, which is also known as the "zero frequency problem."

not only surface information but also latent semantics should be considered when acquiring synonyms from large corpora.


In this context, precision is more important than recall because of the substantial size of the corpus, and some of the extraction errors result from parsing error caused by the parser, whose precision is claimed to be around 90% [2].

we conclude that this method and its performance are sufficient for our purpose.


Using Bayes theorem, we then obtain P (z|n), which corresponds to the latent class distribution for each noun.

in other words, distribution P (z|n) represents the features of meaning possessed by noun n. we can calculate the similarity between nouns n 1 and n 2 by measuring the distance or similarity between the two corresponding distribution, P (z|n 1 ) and P (z|n 2 ), using an appropriate measure.


These words can be noises that may degregate the results of synonym acquisition.

we consider removing such words with low frequencies before the execution of PLSI improves the performance.


In maximum likelihood estimation by EM algorithm, the initial parameters are set to values chosen randomly, and likelihood is increased by an iterative process.

the results are generally local extrema, not global, and they vary every execution, which is unfavorable.


In our work, we require a similarity measure that can reflect the degree of the overlap between two concepts.

the Jaccard coefficient is suitable for our task.


From the table, the MMT amphibian concept covers only toad and frog species (3 members).

the algorithm moves down to a very specific concept, namely the EDR toad concept.


In this project, they introduced the idea of the hyponym-hypernym relationship appears between the entry word and the genus term.

usually a dictionary definition is written to employ a genus term combined with differentia which distinguishes the word being defined from other words with the same genus term.


Our problem is to determine for each noun in definition sentences of a word whether it is a hypernym of the word or not.

our problem can be modeled as two-category classification problem.


As we will discuss in section 3 about features, we are working on very high dimensional feature space, and this often leads to overfitting.

the main reason to use SVMs is that this learning algorithm has a robust rationale for avoiding overfitting [11].


A chunk here stands for a standard phrase.

the word "George" at the beginning of NP chunk "George Bush" has B-NP tag.


If we perform single-staged N+1 classification, the classifier is more likely to identify a testing instance as a nonrelation because of the disproportionate size in samples which often decreases the performance.

in detection stage (or detection only task), we perform binary classification by enriching our training samples by assuming 5 types of relations to be one positive class.


However, the state-of-the-art of parser is always error-prone.

we only use the minimum span parse tree including the NE pairs when calculating the similarity function [4].


This means that we are not restricted to the limited types of relations defined in MUC [1] or ACE [22].

more substantial existing relations can be discovered.


This way, the "Head Word" of the root node of the minimum span tree naturally characterizes the relation between this NE pair in this tree.

we simply count the frequency of the "Head Word" of the root node in the cluster, and then chose the most frequent "Head Word" as the relation type of the cluster.


5) Cluster pruning: Unreliable clusters may be generated due to various reasons such as divergent relation type distributions and the fact that most of the entity pairs inside this cluster are totally unrelated.

pruning is necessary and done in our approach using two criteria.


Questions with unconsidered time granularity classes excluded, the question corpus has 339 questions in total, 145 for training and 194 for testing.

the task is to learn a model from the 145-question training corpus and classify questions in the 194-question test corpus into ten classes: "second", "minute", "hour", "day", "weekday", "week", "month", "season", "year" and "century".


Conventional QA systems cannot answer to the questions composed of two or more sentences.

we aim to construct a QA system that can answer such multiple-sentence questions.


For conventional QA systems, these questions are not expected and existing techniques are not applicable or work poorly to these questions.

constructing QA systems that can handle multiple-sentence questions is desirable.


Depending on the question type, the process in the answer extraction component usually changes.

the accuracy and the efficiency of answer extraction depend on the accuracy of question classification.


Consequently, the accuracy and the efficiency of answer extraction depend on the accuracy of question classification.

as a first step towards developing a QA system that can handle multiple-sentence questions, we propose a method for classifying multiplesentence questions.


[7] compared question classification with five machine learning algorithms and showed that SVM outperforms the other four methods as Suzuki [6] showed.

we also use SVM in classifying questions, as we will explain later.


For example, if the question "I have studied the US history.

i am looking for the web page that tells me what day independence Day is."


These examples show that the target sentence alone is sometimes not a sufficient evidence for core sentence extraction.

in classification of a sentence, we use its preceding and following sentences.


However, since we treat all kinds of questions including the questions which are not in an interrogative form, such as "Please teach me -" and "I don't know -", it is difficult to manually create a comprehensive set of rules.

in this paper, we automatically find the question focus in a core sentence according to the following steps : step 1 find the phrase 5 including the last verb of the sentence or the phrase with "?"


Such Q&A sites include many multiple-sentence questions in various forms.

those questions are appropriate for our experiments where non-artificial questions are required.


The average number of sentences in a multiplesentence question is 3.49.

the task of core sentence extraction in our setting is to decide a core sentence from 3.49 sentences on the average.


The result in Table 6 shows that the model with the core sentence alone is best.

the sentences other than the core sentence are considered to be noisy for classification and would not contain effective information for question classification.


The classifier's output indicates the likeliness of being the question type of a given question.

we compared the proposed model with this model in the preliminary experiment.


The question focuses extracted in the proposed method include nouns which might not be appropriate for question classification.

we regard the improvement on the question focus detection as future work.


Sonority is related to the acoustic intensity of a sound.

by measuring the acoustic intensities of sounds, the sonority of a sound can be estimated [1].


ψ P is called the pivot model because it makes use of phonemes as a pivot during a transliteration process.

ψ P usually needs two steps; the first step is to produce phonemes from source language graphemes, and the second step is to produce target language graphemes from phonemes.


This model only allows word to word and multi-word to word alignments.

some multi-word units cannot be correctly aligned.


This procedure is the same as the one to construct training sets for N-fold cross-validation.

ensembles constructed in this way are called cross-validation committees.


This is because the training data is insufficient or subject to data sparseness problem.

changing the training data or resampling the training data causes the alternation of the trained parameters of the alignment model.


As the syntactic role of each word within Japanese and Korean sentences are often marked, word order in a sentence plays a relatively small role in characterizing the syntactic function of each word than in English or Chinese sentences.

korean and Japanese sentences have a relatively free word order; whereas words within Chinese and English sentences adhere to a rigid order.


Due to the fact that the scope of the application defined by the back-end, the domain of the system is well-defined.

in the presence of vague, ill-defined or misrecognized input from the user, dialogue management, relying on the domain restrictions as given by the application, can interactively request more information from the user until the users' intent has been determined.


For example, in the software trouble shooting task described in [5], a frequent system generated information seeking question is for the version of the software.

there is a need to inquire additional problem relevant information from the user, depending on the interaction history and the problem to be solved.


Although several syntactic relations, such as subject-verb and verb-object, have been also considered in some other systems, they are basically extracted using a small number of hand-built rules.


However, most of the current QA systems only focus on certain relation types, such as verb-argument relations, and extract them from the syntactic tree using some heuristic rules.

extracting such relations is limited in a very local context of the answer node, such as its parent or sibling nodes, and does not involve long range dependencies.


According to our data generation process, we cannot provide data for those NIL questions because we cannot get the sentence collections.

the AE module will fail on all of the NIL questions and the number of the valid questions should be 362 (413 -51).


CLA is designed to tackle the problem caused by indirect association.

if we only make use of the alignment counterpart of the question pattern, we can keep the question keywords in Q so as to reduce the errors caused by indirect association.


The pruned cases are invalid cases which cannot occur in the training data because all the words and POS tags in the training data are valid.

the model built from the training data is not affected by the pruning process.


Among all the features used, only a few might be very relevant to recognizing the non-compositionality of the MWE.

the distance metric used by the nearest-neighbour algorithm which depends on all the features might be misleading.


The threshold is ad-hoc and often not capable of making a clear separation.

the use of threshold often comes with the cost of the inclusion of undesired phrase translations and the absence of good phrase translations in the phrase translation table.


To generalize the notation, we treat word and phrase unit similarly by considering a word as a phrase of length one.

< e, f > hereafter will be referred as <ẽ,f > (0) , which represents the finest level of detail, and <ẽ,f > as <ẽ,f > (N ) , which represents the coarsest level of detail.


In contrast with English where a repeated element tends to appear as a pronoun, in topic prominent languages like Korean, a repeated element has no surface realization.

korean zero elements are often called zero pronouns.


Although missing subject information in languages like Spanish and Portuguese is recoverable from verb morphology, interpretations of missing arguments do not correspond to specific verb morphology in Korean.

marking the place of a zero element is an inevitable process not only for structural representation but for processing the meaning of a sentence.


However, in order for these factors to be useful in developing anaphora resolution algorithms, they must be reliably and consistently annotated into the source data.

the first crucial step for zero pronoun resolution is identifying the exact positions of zero pronouns.


Missing optional arguments and adjuncts are not.

in PKT, missing subject or object elements were marked as zeros while missing locative arguments were not marked when they were omitted.


While ST treats these modal verbs to be included in the preceding verbal clusters, PKT separates them from the preceding verbs and assigns zero subjects for these verbs.

the PKT approach redundantly assigns zero subjects for subjectless predicates.


This is because pairs of wh-question-answer tend to have the same predicates with the same argument structure.

answering utterances of the wh-questions generally contain zero pronouns, whose antecedents appear in the preceding questioning utterances.


Phrase level translation models in statistical MT have demonstrated significant improvement in translation quality by addressing the problem of local re-ordering across language boundaries [8][9][10][11][12].

we also adopt the same concept of phrase used in statistical phrase-based MT [9,11,12] as the basic NE translation unit to address the problems of word fertility and local re-ordering within phrase.


The context-dependent lexical mapping model assumes monotonic alignment in the bilingual training corpus.

the phrase aligned corpus needs to be reordered so that it is in either source-ordered or target-ordered alignment.


It is also noteworthy that the decoder only needs to perform / 2 X expansions as after / 2 X expansions, all combinations of / 2 X phrases would have been explored already.

after / 2 X expansions, we only need to combine the corresponding two partial paths to make up the entire input phrases, then select the path with highest n-gram score as the best translation output.


Now, we extend to include the set of terminal symbols V T' as the translation in target language, disjoint from Now, we define ψ(γ i ) to denote the number of conjunct features being considered in the associated constraint, hence to determine the degree of generalization for a constraint.

the rules, γ i and γ j , are orderable, , we say, the constraint of the rule, γ i , is more specific, while the constraint of γ j is more general.


The lastly ordered setence r belongs to document D in which a block K of sentences follows r. The author of this document assumes that K is a natural consequence of r. However, the sentence selection algorithm might not have selected any sentences from K because it already selected some sentences with this information from some other document.

we search the extract L for a sentence that best matches with a sentence in K. We define succession as a measure of this agreement(13) as follows: Here, we calculate sim(l, k) using cosine similarity.


These experts need Chronological and Probabilistic experts to guide them at the beginning.

we have not compared these orderings in table 2.


Then, the sentence vector s i is defined as where R is the rank of the matrix A.

as before, the vector for a term t j is represented by both the sentence and term vectors can be used to calculate the distances between them.


All the distances from X 3 , X 6 and X 7 vectors are shown in Table 4.

the three significant sentences (third, fifth and sixth) can be included in the summary of our sample article.


Due to dynamic progress in biomedical literature, a vast amount of new information and research results have been published and many of them are available in the electronic form -for example, like the PubMed MedLine database.

automatic knowledge discovery and efficient information access are strongly demanded to curate domain databases, to find out relevant information, and to integrate/update new information across an increasingly large body of scientific articles.


CRFs constrain results to consider only reasonable paths.

total 8 combinations are possible for (s i−1 ,s i ) and the most likely s can be found with the Viterbi algorithm.


During the annotation, we only consider the longest NEs on GENIA.

we find all biomedical named entities in text at the term detection phase.


4 Although this prior probability is quite accurate, it does not have sufficient applicability.

we need to develop another method that can acquire prior probabilities of much more entities and Web can be one alternative.


On the contrary, only 1 super-phrase of a key instance was incorrectly detected.

in-loc can detect potential inclusion at 24.49 applicability and 99.68 accuracy.


However, this method extracts only translations for about 24% of the Japanese words in IPADIC because the EDICT and CEDICT dictionaries are smaller compared with IPADIC.

we also look into the possibility to get the translation words using kanji/hanzi conversion.


With these non-standardization of the English translation, we cannot match the exact words unless we do a morphological analysis in English.

we also look for other ways to increase to number of candidates.


One can create a new word by combining the existing characters but it is hardly that one can create a new character.

these characters are stable in their meaning.


This is to reduce the semantic ambiguity as single character words tend to be polysemous.

direct kanji/hanzi conversion is not so appropriate and we need another approach to handle this type of words.


We can apply the kanji/hanzi conversion method directly to most of the Japanese proper nouns, such as person names, organization names and place names because these names are normally written in kanji characters.

we do not need any effort to translate these words from Japanese to Chinese if we have the character conversion table.


The same character that is used in different languages is assigned with different codes.

it is difficult to convert from one code to another without a conversion table.


Since they did not make any conversion such as traditional characters to simplified characters, some of the characters have the same meaning but different Unicodes.


Since Japanese and Chinese share common Han characters which are semantically heavy loaded, the same characters used should carry the same meaning.

we also proposed a kanji/hanzi conversion method to increase the translation candidates.


The different occurrences referring to a MWT or one of its variants are grouped and constitute an unique candidate MWT.

the candidate MWT produit forestier 'forest product' appears under the following forms: -base form: produit forestier ; -graphical variant: produit fo-restier, pro-duit forestier ; -inflexional variant: produits forestiers ; -syntatic variant: modification: produit non forestier, produit alimentaire forestier, produit fini d'origine forestiére, produit ligneux non forestier ; -syntactic variant: coordination: produit halieutique et forestier, produit agricole ou forestier, le produit et le service forestier.


In such a case, all the markables, regardless anaphors or non-anaphors, will be resolved.

all the occurring markables in a document tends to be linked together.


Due to the problem, the accuracy of each speech act in previous research tends to be proportional to the frequency of each speech act in the training data.

we first focus on how to scale up statistical learning methods to solve the sparseness problem of training data in speech acts analysis.


The shrinkage technique was verified in its efficiency for text classification tasks learned with insufficient training data.

we first build up a hierarchy of speech acts to estimate the weight of features for each speech act by the shrinkage technique.


We report the performance of each system when using the same test data set as that of this paper.

the proposed model achieved the highest performance.


We also proposed to use the discourse stack for easily extracting discourse structure information.

the proposed model with shrinkage and the discourse stack showed the better performance than other speech acts analysis models.


For the input of text-to-speech engine, written texts are not appropriate, because unnatural speech might be produced due to difficult words or long compound nouns, which are unsuitable for speech synthesis.

written texts are automatically converted into spoken texts based on paraphrasing technique [5,6] and then are inputted into speech synthesis.


Japanese is a head-final language and a predicate is placed at the end of the clause.

each predicate in a sentence can be a discourse-unit boundary.


They typically have well developed taxonomies, but are lacking language based descriptions (definitions).

the application of both extrinsic and dictionary based metrics is problematic.


Some of them, such as the lack of conceptual glosses, make it impossible to apply dictionary based metrics in a straightforward manner.

pseudo glosses are generated directly from the conceptual network.


Since the CoNLL dataset is aimed at holistic SRL across all argument types, it incorporates a much larger set of verbs and tagging scenarios; as a result, the semantic role labelling of PPs is far more heterogeneous and realistic than is the case in the treebank.

we conclude that the results of our treebank preposition SRD system are not very meaningful in terms of predicting the success of the method at identifying and semantically labelling PPs in open text.


The unsatisfactory results of our CoNLL preposition SRL system show that the relatively simplistic feature sets used in our research are far from sufficient.

we will direct our future work towards using additional NLP tools, information repositories and feature engineering to improve all three stages of preposition semantic role labelling.


Also, since r is conditionally independent of Ψ q given ¬q, we have P(r |¬q, Ψ r , Ψ q ) = P(r | ¬q, Ψ r ).

we can simplify:  Expanding and simplifying, we establish our Theorem 1: In this section, we evaluate our refinement framework on the temporal precedence relations discovered by VERBOCEAN, and present some observations on applying the refinement to other VERBOCEAN relations.


It turns out that the first rule "piece 3 → mass" in Table 3 is applicable to the instance.


An n × n× k matrix is used to store the transition probability between each state at different time and n × m × k matrix to keep the emission probability.

the space complexity of NSHmm is (( ) ) n n n n m k Ο + × + × × which is k times than that of Hmm.


During training, these named entities are neither tagged nor recognized, so that most of these named entities are segmented into sequences of monosyllabic words.

while the naïve Bayesian classifier encounters monosyllabic context or component words, it would prefer recognizing the possible title as a title.


Recall that the network is trained only on the final interpretation of a sentence.

capturing the observed behavior required manipulation of the frequencies of the four conditions described above during training.


Previous research in classifier combination for POS tagging has focused primarily on combining various statistical classifiers trained on data in the same language.

our approach is novel in its exploitation of differences across languages, rather than differences across statistical methods, to improve performance on POS tagging.


The syntactic format of the interrelation verbs is NP+NP+[ITP]+VP (REL) , namely the interrelation verbs need attach an interrelation case marker "dang" to the nouns related.

the information items of interrelation verbs should include {V (REL) , NP+ITP}.


Such as, the subject-predicate compound verbs usually require accusative objects with zero marker, and verbs of predicate-object type generally require objective objects with objective marker.

the inner structural relationship of compound verbs may produce influence on the objects and their types.


Just as temporal events are usually sequentially correlated, verbs in adjacent linguistic utterances are not independent.

the problem should be further formalized as a sequential learning problem, where we try tag a sequence of verbs (V 1 , …, V n ) with a sequence of tense tags (t 1 , …, t n ).


The advantage of the LOP-CRF approach over the use of a prior is that it is "parameter-free" in the sense that each expert in the LOP-CRF is unregularised.

we are not required to search a hyperparameter space.


In Figure 3, argument z is matched with y , and x with x .

"Ken" and "eiga" come to y and x slots, respectively.


Figure 2 satisfies the second condition, thus the model chooses "sare-ru (PASSIVE)" and passivizes the sentence (8).

"Ken" becomes to be the nominative "ga" as in (7t).


If any of 10 triplets functioned as an LVC, the tuple v, c was merged into the list of light-verbs, assigning an LCS according to the linguistic tests examined in [19].

we collected 40 types of v, c for light-verbs.


In such a case, the probability cannot be calculated, and the model outputs nothing for the given sentence.

of an application of this baseline model to the same set of input sentences, we obtained 320 correct and 215 incorrect paraphrases (Recall: 0.450 (320 / 711), Precision: 0.598 (320 / 535), and F-measure: 0.514).


It is also helpful for information retrieval, especially for fact retrieval systems that take full-sentence queries as their input.

this paper will focus on improving the accuracy of our supervised WSD system for verbs.


Recall that the types of rich linguistic features used by our system were originally inspired by Dang's system, although we implemented them in different ways.

we attribute the more success of our new system mainly to the three specific enhancements we made.


A machine learning model often tends to favor informative features (e.g., rich linguistic features in our case) and fit the distribution of these features well in its training phase.

it is expected that the model will be penalized more heavily when these informative features are used in its training phase but are not accessible in its test phase.


We calculate similarities for the component nouns of the i th NC in the test data with all NCs in the training data.

the modifier and head noun in the i th test NC are each associated with a total of m similarities, where m is the number of NCs in the training data.


Copyrights protect an author's expression of content where content refers to the information contained in a work and expression refers to the linguistic choices of authors in presenting this content.

capturing expression is important for copyright infringement detection.


We define expression as "the linguistic choices of authors in presenting content": content of works and the linguistic choices made while presenting it together constitute expression.

capturing expression requires measuring similarity of works in terms of both of these components.


We hypothesize that, given particular content, authors choose from a set of semantically equivalent syntactic constructs to create their own expression of it.

different authors may choose to express the same content in different ways.


On the other hand, such situation hardly happens when written language is used.

web pages containing many interpersonal expressions are classified as spoken language, and vice versa.


Both methods use the same discrimination rules but map a word to features in a different way.

there are such words that are classified as INAPPROPRIATE by Proposed and are classified as AP-PROPRIATE by Baseline, and vice versa.


When the probability is estimated using the whole corpora, it is influenced by the topic but Business, where topical words in Business are often inappropriate for spoken language.

we think that Baseline is biased to classify topical words as inappropriate.


Denote k as the number of features ∈ F∩ A.

if we define: The distance function (metric) should satisfy the following conditions: (1) 0 ≤ Dis (i,C) ≤ 1; (2) if n=m=k then Dis (i,C)=0; (3) if k=0 then Dis (i, C) =1; (4) β > γ > 0, given that insertion is a more difficult operation than deletion; α=0; 1/(m+n) <β < 1/n; and γ = (1-nβ)/m .


Some skilled users perhaps resort to a Web search engine, but they cannot obtain effective information from a large amount of retrieved irrelevant pages and redundancy information.

it is necessary to provide a system to automatically mine translation knowledge of terms or proper nouns using abundant Web information so as to help users accurately read or write foreign language.


However, terminologies and technical terms often consist of unknown words, and their translations are seldom the combination of each constituent.

the result of direct combination is not very desirable for terminology translation acquisition.


However, our problem is to find Chinese equivalent using English term, so we have to cope with how to obtain the correct boundary of Chinese translations.

the issue and the proposed method in this paper are distinctly different with Nagata's.


With the development of Web and the open of accessible electronic documents, digital library, and scientific articles, these resources will become more and more abundant.

method 3 is a feasible way to solve the terminology translation acquisition, which is also validated by the following experiments.


Because subset redundancy information is an intermediate of estimating terminology translations, its information is basically contained by the longer string candidate with higher frequency.

this problem can be well solved by first sorting and then judging if this item is a subset of the preceding candidates.


But time pressure and costs make it difficult to get glossary building task done fully manually.

there is a pressing need for the tool which is computationally supported.


Tokenization and POS Tagging is very small, and the occurrence of which is also rare.

the problems of bigrams, tri-grams and 4-grams are primarily taken into considerations in our work.


Because of the poor performance in our first test, Dice formula was not used in hybrid method any more.

we have delivered three different experiments using like "LogL + Liguistic Filter", "Chi + Liguistic Filter", "MI + Liguistic Filter" methods.


By grammatical appropriation, we refer to compound noun phrase or compound verb phrase, since with majority of multi-word terms have these structures.

hybrid method using linguistic filters proved to be a suitable method for selecting terminological collocations, and it has considerably improved the precision of the extraction.


The frequent syntactic patterns of terminology and the stop-word list were used to filter out the irrelevant candidates.


Existing search engines, such as Google and Yahoo!, do not perform lemmatization or stemming for indexing Web pages in Mongolian.

web pages that include only inflected forms of a query cannot be retrieved.


Because loanwords are linguistically different from conventional Mongolian words, the suffix concatenation is also different from that for conventional Mongolian words.

exception rules are required for loanwords.


There are a number of suffixes that can concatenate with both verbs and nouns, but the inflection type can be different depending on the part of speech.

verb phrases can incorrectly be lemmatized as noun phrases and vice versa.


We remove the suffix "ийн (genitive)" and add "и" to the end of the content word.

the noun "экологи (ecology)" is correctly extracted.


For each query,  we used as the relevant documents the abstracts that were annotated with the query keyword in the keywords field.

we were able to avoid the cost of relevance judgments.


Any lemmatization method improved the MAP for both KQ and LQ.

lemmatization was effective for information retrieval in Mongolian.


This algorithm will back off to forward maximal matching algorithm if the goodness function is set to word length.

the former may be regarded as a generalization of the latter.


In principle, a substring with a negative DLG do not bring any positive compression effect by itself.

only substrings with a positive DLG value are added into our word candidate list.


Note that DLG criterion is to perform segmentation with the intension to maximize the compression effect, which is a global effect through the text.

it works well incorporated with a probability maximization framework, where high frequent but independent substrings are effectively extracted and re- combined.


One problem with their method is that the sub-trees enumerated in Collins and Duffy's kernel computation are context-free, that is, they do not consider the information outside the sub-trees.

their ability of exploring syntactic structured information is much limited.


Furthermore, we know that the extending story is chosen by cosine similarity, which results that the extending story and the extended story are usually topically linked from the same perspectives, seldom from different perspectives.

the method of information extending may sometimes turn the above first problem worse and have no impact on the second problem.


We expressed different characters between two terms and their context (window size ±1) as features, shown in Table 3.

to represent an omission, "φ (null)" is considered a character.


However, the trigram is defined as the joint probabil- rather than the commonly used conditional probability 1 2 .

the so-called trigram in Qu and Grefenstette (2004) is basically a substring unigram probability, which we refer to as the n-gram (n-character) sum model (SUM) in this paper.


As a function of model, c PP measures how good the model matches the test data.

c PP can be used to measure how good a test name matches a training set.


This may be due to the fact that the PP measures the normalized conditional probability rather than the sum of joint probability.

the PP method has a clearer mathematical interpretation than the SUM method.


In CNOR (see Table 8), we note that the Chinese transliterations of English origin use only 377 Chinese characters, so data sparseness is not a big issue.

bigram SUM and bigram PP methods easily achieve good performance for English origin.


Note that no Viterbi search involved here and the state sequence is also known.

even Forward/Backward algorithm is not required and rating the chunks is therefore computationally efficient.


Omission of other grammatical information is, on the contrary, to be resolved in pragmatic level because some extra-linguistic knowledge is required.

we concentrate in this paper the resolution of gapping by means of syntax and semantics.


Since w 1 k+1 combines w 2 k+1 , we can conclude that w 2 k+1 = p ′ q ′ w 2 k .


Since w 1 k+1 combines w 2 k+1 , we can conclude that w 2 k+1 = w 2 k q ′ p ′ .


For example, we have the string " (specialistlevel)/w 1 /w 2 /w 3 / (discussion)", and " (specialist-level)" is TOP and " (discussion)" is NEXT.

we can have the feature "TN0:3+:D1-L-HIGH" for TOP and NEXT, because the word distance is 4(3+) and " (specialist-level)-(discussion)" belongs to the bucket "D1-L-HIGH".


Recent reviews by (Wong et al., 2007b) show that existing research on unithood are mostly carried out as a prerequisite to the determination of termhood.

there is only a small number of existing measures dedicated to determining unithood.


some measures are required to gauge the strength of word combinations to decide whether two word sequences should be merged or not.


This provides a good indication on the unithood of s since the more s appears in usage, the likelier it becomes that s is a stable unit instead of an occurrence by chance when a x and a y are located next to each other.

the derivation of OU (s) using O L and O G will ensure a comprehensive way of determining unithood.


On the other hand, if a candidate word is not related to any of the chains, a new chain is created for the candidate word.

a lexical chain is made up of a set of semantically related words.


Each occurrence of a word in a document, will increase the length of the chain by one.

the length of a chain gives a composite measure of the number of documents in which the chain occurs and the number of occurences of words in the chain in these documents.


These messages are written by a wide population of net users and represent a good variation in writing styles, choice of words and grammar.

we feel that the 20NG is a representative corpus for the purpose.


It can be seen that a speedup of more than 74% is achieved with the lexchain based features 4 .

the results show that the running time of the clustering algorithms is drastically reduced while maintaining or improving the clustering performance through the use of lexchain based features.


Simultaneously using both of these two types of pairs for training will adversely affect the accuracy of classification.

we propose splitting the dataset first according to similarities of pairs, and then constructing a classifier for each cluster (sub-dataset).


English) which are in the same class structure as the unlabelled documents in another language.

how to ex-ploit the existing labelled documents in some language (e.g.


Secondly, there is no linear ordering of the word occurrences.

the naive Bayes classifier can be further formalized as follows: The estimates of P (c) and P (w|c) can be referred to (McCallum and Nigam, 1998) Some extensions to the naive Bayes classifier with EM algorithm have been proposed for various text categorization tasks.


Users can not get access to news pages in time.

another kind of crawler called instant crawler is developed.


The crawler has to make a decision about which URLs should be downloaded first, which URLs should be downloaded later and which URLs are not worthy downloading at all.

there is some work in ordering the frontier for a crawl according to the predicted quality of the unknown pages [Cho, Garcia-Molina and Page, 1998;Eiron et al., 2004].


However, among the proposed translation words, there may be irrelevant ones.

one can take advantage of several translation resources and tools in order to produce better query translations.


In addition, language modeling also provides a solid theoretical framework for integrating more aspects in IR such as query translation.

we will use it as our basic framework in this study.


It tests their validity on new validation data.

the translation candidates are rescored and filtered according to a more reliable weight.


This is achieved by the following TSUBAKI's characteristics: API without any restriction: TSUBAKI provides its API without any restrictions such as the limited number of API calls a day and the number of results returned from an API per query, which are the typical restrictions of the existing search engine APIs.

tSUBAKI API users can develop systems that handle a large number of web pages.


It took two weeks to finish the conversion.

100 million web standard format data were obtained.


On the other hand, syntactic parsers can produce different dependency relations for each sentence.

the difference between these sentences can be grasped by looking at their dependency relations.


document frequencies of dependency relations are relatively smaller than those of single words.

tSUBAKI naturally gives high score values to web pages that include the same dependency relations as the one included in the given query.


Moreover, private search algorithms are also the problem since API users cannot know what goes on in searching web pages.

it is difficult to precisely assess the contribution of the user's proposed method as long as the method uses the existing APIs.


The released infrastructure, however, allows users to access only the web pages in the Amazon.com Web site.

tSUBAKI is different from the infrastructure of the Web Laboratory project in terms 5 http://projects.netlab.jp/rast/ 6 http://search.wikia.com/wiki/Search Wikia 7 http://www.archive.org/index.php   We have described tSUBAKI, an open search engine infrastructure for developing new information access methodology.


In this work, we use alignment for WER to identify the correct words and calculate WWER.

wwER equals wER if all word weights are set to 1.


WWER calculated based on ideal word weights represents IR performance degradation when the ASR result is used as a query for IR.

we must perform ASR to minimize WWER for speech-based IR.


This fact shows that each keyword has a different influence on IR and    In ASR for IR, since some words are significant, each word should have a different weight.

we assume that each keyword has a positive weight, and non-keywords have zero weight.


In our method, the weightings of the Weighted Finite State Transducer (WFST) are designed on two levels and simpler than those for conventional WFST-based methods.

our method needs much fewer training data, which enables rapid prototyping of LU modules.


We need fewer data to train WFST, because its weightings are simpler than those in conventional methods.

it is easy to develop an LU module for a new domain with our method.


This description enables an SLM for ASR and parsing WFST to be automatically generated.

a developer can construct an LU module rapidly with our method.


The sum of weights for concepts is α c (1.335 − 2θ c ) because the weight for "month=2" is α c (0.81 − θ c ) and the weight for "day=22" is α c (0.525 − θ c ).

cumulative weight w i for this input sequence is w i s + α w (4.1 − 5θ w ) + α c (1.335 − 2θ c ).


We train a subword based translation model and use the model to translate the sub-word sequence.


We have analyzed this points in the last section.

in order to translate unknown words, our approach is to split longer unknown words into smaller pieces, and then translate the smaller pieces by using Character or Subword models.


Using the Hyperword CWS can generate a higher quality of translation models than the Character and Subword CWS.

we cannot use the character and subword-based CWS in Chinese SMT system due to their overall poor performance.


We also used named entity translation together with the subword translation.

we could compare the effect of subword translation under conditions of with or without named entity translation.


In Snippet 3 and Snippet 4 , "," and "•" in the underlined terms are disregarded, so the Web counts based on such Web documents are noisy.

noise in the Chinese Web counts causes systems based on Web counts to produce more errors than our systems do.


Our proposed method can filter out such noise because our systems take punctuation marks and the contexts of transliterations in Web mining into consideration.

our systems based on features mined from the Web were able to achieve the best performance.


In Table 6, we can see that W 1 , a set of Web pages retrieved by Q 1 , tends to give more relevant information than W 2 and W 3 , because Q 1 can search more Web pages containing both S and h i in the top-100 snippets if S and h i are a correct transliteration pair.

its performance tends to be superior in Table 6 if W 1 is used, especially for ECSet.


Fry, 1968;Gunning, 1959) can affect the quality of draft translations.

the number of words in a sentence, length of words, number of verbs in a sentence, number of commas, etc.


The underlying intuition is that all the sentences X 1...n that are "similar" to any other sentence Y i should be in the same cluster even though X j and X k may not be "similar" to each other.

we find the connected components in the similarity matrix and label them as individual clusters.


Surprisingly, little has been done in applying probabilistic models trained to maximize the margin to simple classification tasks (to the best of our knowledge).

since the Naïve Bayes model has been shown to be a successful algorithm for many text classification tasks (McCallum and Nigam, 1998) we suggest learning the parameters of Naïve Bayes model to maximize the probabilistic margin.


Furthermore, new NEs are generated everyday, but bilingual dictionaries cannot update their contents frequently.

it is necessary to construct a named entity translation (NET) system.


Named Entity Translation for IR Most Korean NEs originate from Hanja.

the most straightforward way to translate a Korean name into Chinese is to use its Hanja equivalent.


Obviously, the above methods cannot cover all possible translations of NEs.

we propose a pattern-based method to find the translation from the Web.


Since the Chinese translations of some NEs cannot be found by patterns, we find their Chinese translations indirectly by first finding their English translations and then finding the Chinese translations.

we must generate K-C patterns to extract K-C translation pairs, as well as K-E and E-C patterns to extract K-E and E-C pairs, respectively.


However, for other NEs, especially from non-CJK, this is not the case.

we split the translation process into two stages: the first translates the NE into its English equivalent, and the second translates the English equivalent into Chinese.


For the given English term "Kosovo", the MI scores of "科索夫", "科索伏", and "科索沃" are computed; "科索伏" achieves the highest score, while the score ratio of the other two candidates are much lower than the threshold.

only "科索伏" is treated as Kosovo's translation and used to build the final Chinese query to perform the IR.


The context-dependency property indicates the context association between product features and opinion words.

with the revised mutual information, the implicit features can be deduced from opinion words.


Annotation set for product features and opinion words Unlike English, Chinese are not separated by any symbol.

the reviews are tokenized and tagged with part-of-speech by a tool ICTCLAS 2 .One example of the output of this tool is as (d).


However, it has limitations due to the diversity of expressions.

we propose a machine learning based method that models the polarity-shifters.


Our attempt in this paper is not to use the information included in those substructures of sentences, but to use the word-level polarities, which is a resource usually at hand.

our work is an instantiation of the idea to use a resource on one linguistic layer (e.g., word level) to the analysis of another layer (sentence level).


The reason is that the majority voting cannot take into account negation expressions or adversative conjunctions, e.g., "I have not had any ..." in the example above.

taking such polarity-shifting into account is important for classification of sentences using a polarity dictionary.


In this method, the model is learned so that the predictive ability for sentence classification is optimized, instead of the predictive ability for polarityshifting.

this model can remain indecisive on the classification of word instances that have little contextual evidence about whether polarityshifting occurs or not.


Therefore the external knowledge, such as word-level polarity, could be more valuable information for classification.

it is expected that the sentence-wise model and the hybrid model will outperform n-gram classifier which does not take word-level polarity into account, more largely with few training data.


When shopping for a product, one might be interested in the physical characteristics of the product or what features the product has, rather than opinions about how well these features work or about how well the product as a whole functions.

if one is looking for a review that contains more factual information than opinion, one might be interested in reviews like Example (3).


In the experiments described in this paper we used 2317 reviews of mobile phones of which 1158 are negative and 1159 are positive.

random choice would have approximately 50% accuracy if all items were tagged either as negative or positive 2 .


In addition, the classifier found words that were used in reviews to describe situations connected with a product and its features: for example, (service) was often used in descriptions of quite unpleasant situations when a user had to turn to a manufacturer's post-sales service for repair or replacement of a malfunctioning phone, and (user) was often used to describe what one can do with some advanced features.

the classifier was able to capture some product-specific as well as market-specific sentiment markers, however, it was not able to distinguish the context these generally objective words were used in.


Formally: where x(a, r) stands for the similarity between a and r according to the metric x.

qUEEN allows us to combine different similarity metrics into a single measure, without having to adjust their relative importance.


Among 66,664 bilingual dependency patterns, 20,968 patterns were generalized into 12,631 unique generalized patterns by applying the extracted paraphrases 2 .

the 66,664 bilingual dependency patterns were compressed into 58,324 generalized patterns with 12.5% compression ratio.


The probability distribution over possible worlds x specified by the ground Markov Network M L,C is given by where n i (x) is the number of true groundings of F i in x, x {i} is the true value of the atoms appearing in F i , and In the case of Chinese NER, a named entity can be connected to another named entity for instance, because they share the same location salient word.

in an undirected graph, two node types exist, the LOC nodes and the location salient word nodes.


We group the pseudo-documents in our database by the relation label that the instance pairs were given.

we end up with several bags of pseudo-documents, where each bag corresponds to a single relation type of interest.


Moreover, we would like to model the structure of abstract sentences rather than modeling just the section label for each sentence.

the task is more suitably formalized as a sequence labeling problem: given an abstract with sentences x = (x 1 , ..., x n ), determine the optimal sequence of section names y = (y 1 , ..., y n ) of all possible sequences.


For example, the number of unique mappings between semantic roles and syntactic categories in our corpus is 5,065 for specific corpus categories, and 2,289 for abstracted categories.

the definition of an abstraction layer, in conjunction with a flexible query mechanism, allows us to induce lexical characterisations of the syntax-semantics mapping -aggregated and generalised from disparate corpus annotations.


In Asian languages these classifiers are ubiquitous and used with common nouns.

the number of classifiers is much larger than in Western languages.


For instance, in our Japanese database, the classifier " (ten)" agrees with shops such as "drug store", "kiosk" and "restaurant", and these nouns also agree with " (ken)", since " " is a classifier which agrees with any kind of building.


0.98 (Chinese), 0.91 (Japanese) and 0.99 (Thai).

we can conclude that the extracted superordinatesubordinate relations are reliable enough.


However, the average of R(c a * c d ) in "indirect" cases is not so high for both Chinese and Japanese, as a large amount of pairs are classified into case (A).

it is not effective to infer new superordinate-subordinate relations by transitivity.


"railroad") can be based on the number of distinct languages exhibiting this association.

we rank the candidate translations generated via different languages as in Figure 1 as follows: For a given target compound word, say f c with a set of English glosses G obtained via multiple splitting options or multiple component word translations, the translation probability for a candidate translation can be computed as: 1 In order to avoid inflections as component-words we limit the component-word length to at least three characters.


These constraints constitute heuristics to avoid, rather than solve, complex linguistic phenomena.

a nugget is always discarded if it does not contain a verb, or contains any pronoun.


A thorough evaluation of answers to definition questions would be complex, prone to subjective assessments, and would involve significant human labor (Voorhees, 2003).

the quality of the text snippets in the context of definition questions is tested on a set, DefQa1, containing the 23 "Who is/was [ProperName]?"


Some patterns may be more indicative of causes than others.

it may be useful to assign different weights to the patterns for better answer candidate extraction, but currently this must be done by hand .


When successfully found, correct answers are not always assigned sufficiently high confidence scores to ensure their high ranks in the final answer set.

overall question answering scores are still suffering and considerable effort is being directed towards improving answer extraction and answer merging, yet little attention is being directed towards retrieval.


However, making the right guess about the relationship is easier if the U.N. body is grouped with army and government.

the right guess in this example is management.


Finally, N is the length of the document d. An entity pair E in the relational corpus is similar to a document d in the text corpus.

in other words, the corpus D R is comprised of returned snippets for all entity pairs E R with the same relation label R. given the parameters α and β, we obtain the distribution of entity pair E as follows: N E is the number of words in the returned snippets for E; and w n is the n th word in S E , the returned snippets of E. Table 1 summarizes notations used in the paper.


The feature captures the interaction between two entities at the semantic level rather than at the word level.

combining the latent topic f eature with syntactic features and lexical features should achieve a better performance than using the features separately.


In languages such as Chinese and Japanese, blank spaces are typically used to indicate that the entry software should perform conversion, thus guaranteeing that the sequence between two delimiters will consist of only one language 4 .

assuming that a text fragment between two delimiters is written in just one language is natural for users.


On the other hand, Failure B errors are a concern as such failures might increase the number of control actions to beyond the number required by a conventional method.

the effectiveness of introducing TypeAny depends on a trade-off between fewer control actions at language switching points and potentially more control actions due to Failure B errors.


Only a small corpus is typically available to estimate P (l m |l m−1 m−k max +1 ), where k max is the longest k-gram in the language sequence to be considered.

the transition probability is estimated on-line, making use of language that will be corrected interactively by the user.


However, since our system uses PPM and learns on-line, our system learned that "tofu" had to be English, and such detection errors occurred only at the beginning of the text.

there was a substantial decrease in the number of necessary control actions with Ty-peAny, over 93%, for both articles.


Now, instead of using the seed f req (p, c) as the score for a particular pattern with respect to a class, we can rescore patterns using the probabilities P (class|pattern).

the final score for a pattern of retained patterns across all classes for {English,Hindi} were {455,117} respectively.


For example, the article "Dominican Republic" belongs to three categories: "Dominican Republic", "Island countries" and "Spanish-speaking countries".

the article Dominican Republic appears in three different category pages.


Our 12,906 articles taken from Wikipedia belong to a total of 18,810 associated categories.

clearly some articles have more than one category; however some articles also do not have any category.


In the context of temporal annotations, we extend the scope of the filter such that it effectively applies to all word classes that we deem unlikely to occur as part of a temporal entity.

the NC filter is actually defined stronger for events than for timexes, in that it further blocks projection of events to pronouns, whereas pronouns may be part of a timex such as jeden Freitag 'every Friday'.


We ran the extended Espresso algorithm starting with 971 positive and 1069 negative seed relation instances for action-effect relation and 860 positive and 74 negative seed relations for action-means relation.

we obtained 34,993 cooccurrence patterns with 173,806 relation instances for the action-effect relation and 23,281 coocurrence relations with 237,476 relation instances for the actionmeans relation after 20 iterations of pattern ranking/selection and instance ranking/selection.


"strict" indicates correct instance relations while "lenient" 3 indicates correct instance relations -when a judge appends the right cases.

of this strictness in judgement, the inter-assessor agreement turned out to be poor.


If a 4-gram language model is used (common in many current SMT systems), the time complexity is as high as O(n 15 ).

with this time complexity translating long sentences is time-consuming even with highly stringent pruning strategy.


• mWER (multi-reference word error rate) (Nießen et al., 2000): This measures the edit distance with the most similar reference translation.


The translation model here is between document words and queries words.

the user profile as a translation model in our approach will consist of triples of a document word, a query word and the probability of the document word generating the query word.


For the majority of the world's languages, the number of linguistic resources (e.g., annotated corpora and parallel data) is very limited.

supervised methods, as well as many unsupervised methods, cannot be applied directly, leaving these languages largely untouched and unnoticed.


In addition, we found precision increased when we included two or more search terms per query, with the most successful queries being those which combined grams and language names.

for example, although NOM alone returned a large number of linguistic documents, NOM combined with ACC (or any other high frequency term), or a language name, returned a far less noisy and far more relevant set of documents.


Further, many true IGT instances do not match any of hand-written templates due to the issues mentioned in the previous section.

both precision and recall are quite low (see Table 4).


table 4 in fact lists all the possible feature combinations.


8 The corruption not only affects the target lines, but also the layout of IGT (e.g., the indentation of the three lines).

features in F2 and F4 are not as effective as for the development set.


To extract BOW features from sentences including the given phrase within Web snippets, a morphological analyzer MeCab 3 was firstly used; however, it resulted wrong POS tags for unknown words, and hurt statistics.


However, they have conducted only a posteriori comparison based on performance evaluation, and we are afraid that these find-ings are somewhat limited to their own experimental settings which may not be applicable to completely new settings, e.g., one with a new set of contexts extracted from different sources.

general quantitative measures which can be used for reduction and selection of any kind of contexts and context categories are strongly required.


The following methods, MI, IG, and CHI2, are radically different from the above ones, in that they are designed essentially for "class classification" problems.

we formalize distributional similarity as a classification problem as described below.


Then in the next iteration, we add the acquired NCs into the training dataset for use in classifying more instances.

in each step, the number of training instances increases monotonically.


Another benefit  Figure 5: TNR for each SR for the binary task (negative instances, CSIM +SCOLL CT method) of this method is that it interprets NCs without word sense information.

we conclude that the CSIM method is more flexible and robust.


In lookup table approach, each word and its related stem are stored in some kind of structured form.


We have a rule in rules repository as: where it is matched with the word " ﯽ ﻣ ﻢ ﻧ ﺍ ﻮ ﺧ "(mixän-am: I read).

we find a matched rule f o r " ﻥ ﺍ ﻮ ﺧ "(xän).


At the end of each step, C is increased by one.

in each step, C points to one letter of the word that we call this letter L.  At first step, first pointer P1 finds one edge between root edges that its letter is equal with L. P1 goes down on tree by that edge.


All of them form a special state space for each acquired rule.

the confidence score (θ) for the rule can be easily computed to evaluate the accuracy expectation to apply it in an automatic parser.


Although these 211 rules only make up 29% of all the 735 acquired rules, they cover about 97% positive examples in the training set.

almost all the useful information can be reserved for further rule expansion and refinement.


Japanese texts, which we focus on, are written without using blank spaces.

japanese NER has tight relation with morphological analysis, and thus it is often performed immediately after morphological analysis Yamada, 2007).


In order to recognize these NE classes, it is essential to use the information obtained from the previous context.

we utilize information obtained from the NER for the previous context: cache feature and coreference relation.


For instance, by the case analyzer, the postpositional particle "-ha" in (6) is recognized as meaning nominative and "ICAO" is assigned to the "ga (nominative)" case of the case frame of "haken (dispatch).

"the caseframe features, "ORGA-NIZATION" and "LOCATION" are added to the features for the morpheme "ICAO."


We introduced four types of structural information to an SVM-based NER system: cache features, coreference relations, syntactic features and caseframe features, and conducted NER experiments on three data.

the performance of NER was improved by using structural information and our approach achieved a higher F-measure than existing approaches.


Given the complicated nature of the ESL error proofing task, about 60% of ESL sentences in our set that contained determiner errors also contained other types of ESL errors.

we were forced to slightly revise the typical precision/recall measurement in order to evaluate performance.


They are usually randomly proposed and may be irrelevant to the utterance.

they may not attract users' attentions and have no effects on advertisements.


The former employs the original category tree, while the latter expands the category tree with titles, links and snippets.

the former contains 7,839 nodes and the latter 78,519 nodes.


As discussed, for each of node in the category tree, there is an accompanying HTML file to show some related web sites and even sponsors.

we can also use the category tree to put sponsor links into the HTML files, and just fetch the sponsor links from the HTML file on the node to the customers.


In our application scenario, we are interested in terms whose constituent words have strong collocation relations and can be translated to another language in stable single word or multi-word translation equivalents.

we define "term" as a word/phrase that carries a special meaning.


In Table 5, the average scores for "All Term" are 38.8% and 48.3% for NCValue and NTCValue respectively.

nTCValue method improves global TE by 24.4% when compared to the origin nCValue method.


We define a language model for each Dmozcategory and select labels for clusters according to the probability that this language model would generate candidate labels.

our method can select more meaningful labels for clusters because we use labels generated by human-tagged categories of Dmoz.


They are also slow in generating clusters and labels because they use entire document contents in the process.

it is difficult to apply these approaches to search engine applications.


However, " ", which means "to disappear", is not ideal for a person's name.

kanji characters must be selected carefully during transliteration into Chinese.


In Figure 2, (d) is such a representation for the two basic alignments.

the structure of word aligned sentences can be transformed to an operation sequence, which represents the bilingual parsing correspondences.


stack operations) are decided, the bilingual tree structures are formed at the same time.

results of these operations are the final translations which we really need.


In contrast with traditional beam search decoder, we generate our translation candidates from the same input, instead of all allowed word pairs elsewhere.

the pruning is much more reasonable for each beam.


In general, the complexity to calculate one probability is dominated by the output layer dimension since the size of the vocabulary (here N =273k) is usually much larger than the dimension of the hidden layer (here H=500).

the CSLM is only used when the to be predicted word falls into the 8k most frequent ones.


The creation of a view may be conceptualized as the application of a view description to a specific text.

the application of a module implies the creation of a set of text views.


It seems that the teaching of text linguistics contributes to sharpen the attention of students towards the building of well-formed texts and to stimulate their own text production.

a tool that allows the student to perceive text units that contribute to and maintain text coherence and to navigate between them, can be supposed to be an important didactic tool for teaching reading of foreign language texts, as well as producing written texts in the foreign language.


The criterion implies that both E 0 and E 1 are the synset for L 0 which can be defined by a greater set of synonyms in S 1 .

the relatively high confidence score, CS=4, is assigned for this synset to the lexical entry.


The system manages the synset assignment according to the preferred score obtained from the revision process.

the community WordNets will be accomplished and exported into the original form of WordNet database.


The word alignment and syntactic projections are not perfect.

the trees generated, and the rules read off of them, may be incomplete or inaccurate.


but also try to attain simplicity and speed properties of Incremental algorithm.

this model provides a simple and linguistically rich approach to GRE.


The only modification that we add that when a relation R is included, the MakeRefExpr() should be called again for the relatum.

if Val(E(N i , N k i+1 )) expresses a relation of r with r′ ′ ′ ′ then we have to call MakeRefExpr (r′ ′ ′ ′,p,T,L) again to find description for r′ ′ ′ ′.


Now, if we search the branches of new combined tree in parallel, it's obvious that we can always find the target node at lowest possible level.

we can generate shortest length description using our algorithm.


Moreover, comparing the F-scores reported in the literature for the five parsers revealed that the F-score (70.1) of this parser was more than 10 percentage points lower than the score of the second worst parser MINIPAR.

it is obvious that the high coverage in APP is achieved at the cost of preciseness and lack of detail in the output.


It results that syntactic symmetry is not enough to resolve ambiguities of conjunctive structures and semantic reasonableness is hard to be evaluated.

we propose a divide-and-conquer approach which takes the advantage of using structure information of partial sentences located at both sides of conjunction.


Furthermore we believe that simple cases can be solved by simple methods which are efficient and only complex cases require deep syntactic and semantic analysis.

we develop an algorithm to discriminate simple cases and complex cases first.


( The long pattern has low applicability and hardly can evaluate its precision.

a rule generalization process is applied.


Due to limit amount of development data, many local maximum and global maximum are achieved by different values of w1, w2, w3.

we use a clustering algorithm to cluster the grid points of (w1, w2, w3) which produce the best performance.


In real practices, parser may accept sentences with ambiguous word segmentation and pos tagging to avoid the error accumulation due to early commitment on word segmentation and pos tagging.

parsers require much more information to resolve much more ambiguous conditions.


It is not only time consuming but also knowledge intensive.

we propose a contextdependent rules matching approach to handle simple cases to get fast returns.


Our solutions include the rule-based method and cooperate with semantic and syntactic analyses.

in the future we will try to enhance the syntactic and semantic analyses.


The relations among the words in a chunk are not marked for now and hence allow us to ignore local details while building the sentence level dependency tree.

in our dependency tree each node is a chunk and the edge represents the relations between the connected nodes labeled with the karaka or other relations.


They deal with the compression of one sentence, while we summarize some sentences into a onesentence summary.

our summarization ratio is inevitably lower than theirs, as it is considered to be more difficult as a summarization task.


Here, we limited 77 outputs judged as good similar instances in evaluation of the process of retrieving similar instance, because we evaluate specifically the parts of phrase alignment and combination.

the examinee categorized them based on how proper the output summary is to the input news article: 1) quite proper 2) slightly proper 3) not very proper 4) not proper of judgment, 48 outputs out of 77 are evaluated either 1) quite proper or 2) slightly proper.


At the same time, during classification in Ca, other different features (other than 'China', 'democracy', etc) would come to distinguish between sentence 1 and 4, so cluster centers of sentence 1 and 4 would have bias toward the distinguishing features.

their adjacency value tended to be lower in Ca, and in fact, they fell apart in the sequence.


a Japanese term sequence of an answer candidate A.

the Japanese term sequence We exclude the answer term sequence A from the calculation of the translation probability, because the English terms that corresponds to the answer should not be appeared in the question sentence as the nature of question answering.


Although the GTK shows promising results for SRL, one big issue for the kernel is that it needs exponential time to compute the kernel function since it need to explicitly list all the possible variations of two sub-trees in kernel calculation (Zhang et al., 2007).

this method only works efficiently on such kinds of datasets where there are not too many optional nodes in production rule set.


The second sense is rarely used, and thus if the synonymy pair extracted from the second definition is used as a synonym relation, an inappropriate matching through this synonym might be caused.

only the pairs of synonyms/hypernyms whose distributional similarity calculated in Section 2.2 is high are utilized for the flexible matching.


This corroborates our assumption that (disregarding errors) all occurrences of a MWE share the same tree structure.

we started storing the tree structures in the SemLex entries and employ them in pre-annotation (D).


This also allows us to use preannotations (B) and (C), but we have decided not to use them at the moment, in order to be able to evaluate each pre-annotation step separately.

the following section reports on the experiments that employ pre-annotation (A) and (D).


For a real article, the matrix would be less sparse than a fake article and so is the case for the reconstructed matrix.

the statisticsmean, median, minimum and maximum computed from the reconstructed matrix have higher values for real articles than a fake articles.


Given a subset of features, Maxent classified 74.5% of the documents correctly compared to 82% for AdaBoost.

adaboost was chosen as the classifier for further experiments.


Since a text document often belongs to multiple categories in real tasks such as web pages and international patent categorization, text categorization is generally defined as assigning one or more predefined category labels to each data sample.

developing better classifiers with a generalization ability for such multi-label categorization tasks is an important issue in the field of machine learning.


(8), we can also obtain the approximate forms,F M (Θ, w) andF L (Θ, w), of the F M -and F L -scores, and then present similar objective functions to that for the F μ -score.

in the next section, we examine experimentally the performance of classifiers obtained by estimating w to maximizeF M (Θ, w) andF L (Θ, w).


As shown in Table 1, there were more category label combinations for JPAT than for Reuters or WIPO.

there were fewer data samples for the same category label assignment for JPAT.


Notice that for any vertex v i ∈ I, R i correctly covers t i and no other rule appearing before R i covers t i .

t i is happy under L. It follows that |Happy(L)| ≥ |I|.


On the other extreme, if h(t) ≈ 0, then only a small fraction of the rules that cover t annotate it correctly.


In this case, if the similarity of a document pair is equal to or greater than 80% and 3 terms are selected from one document, the other document must contain at least one of the 3 terms.

prefix-filter can remove document pairs where one document does not contain any of the 3 terms selected from the other.


In order to identify the same document pairs correctly, a deep similarity function such as edit-similarity is essential.

the number of similarity calculations should be decreased as much as possible.


Although multi-level prefixfilter using Score(n,w) succeeded in the reduction of processing time, Score(d,n,w) failed because of too many score calculations.

we used Score(n, w) in the following experiments.


In addition, it showed that the optimal number of applied prefix-filters did not depend on the target field or the size of the target database.

multilevel prefix-filter proved to be more effective than the current prefix-filter without losing the advantages of the current prefix-filter (no detection loss, no extra parameter).


As mentioned, the address field was longer than that of the company name field on average, and it contained more duplicated documents.

we expect that the proposed method is effective in the following situation: (i) the length of each document (record) is short, (ii) the number of duplicate documents has been reduced beforehand by simple filtering methods such as deleting exact match documents or documents different only in space, and (iii) detecting the remaining duplicate documents by using a deep similarity function such as edit-similarity.


Here, we explain how to obtain x of edit-similarity.

first, (|d1 ∩ d2| denotes the number of common terms in both d1 and d2), and ,


Although namesakes have identical names, their nicknames usually differ.

a name disambiguation algorithm can benefit from the knowledge related to name aliases.


According to our definition, a node that lies n hops away from p has an n-order co-occurrence with p. Considering the fact that a single web page might link to many pages with diverse topics, higher order co-occurrences with p (i.e.

nodes that appear further from p) are unreliable as aliases of p. we limit C(p), the set of candidate aliases of p, to nodes which are directly  connected to p in the graph.


The classifiers were tested on an 15% unseen test set.

the training set consists of approximately 57, 000 data points (patient visits), which is a more realistic dataset compared to the previously used datasets -e.g.


(hyponym is one of hypernym) Note that hyponym and hypernym match only with NPs.


The SenseNet can sense either positive or negative "sentiment", but it cannot classify different emotions.

this paper explains how the SenseNet can be employed to sense emotions from text.


In our case, we consider each triplet to indicate an event encoding the information about "who is doing what and how".

the output given in Table 2 has two events, which are dependent to each other as indicated by 'dependency' keyword in the action attribute of Triplet 1.


From SenseNet we get the valence for terrorist as -3.620.

in this case we set op as "Desirable" because of having a negative valenced event associated with a negative valenced agent.


Heuristically we kept the threshold 0.10% to signal familiarity and unfamiliarity of an object.

"diamond ring" and "thief" gets "Attractive" and "Unattractive" set for oa, but "restaurant" gets 'null' accordingly.


Although in txt there might be multiple e described and we also deal with such cases to get the resultant emotion types from txt, but we don't discuss that in the scope of this paper and describe the simple cases.

the rules for emotion types are given considering an event e, for example, the program x senses 'Joy' for e if following condition is true: [Linguisitc_Token_found_for_Joy (txt) and No_Negation_Found (txt)] or [vr= true and sr= "Pleased" and sp= "Desirable" and cs= "Self"] (i.e., literally joy means being 'pleased about a desirable event'.)


Vertices that dangle from the hub-37% of the core members-would be dropped from the second MCL computation if the latent adjacency is not used, which, on the other hand, assures a high recall rate (0.88).

we have adopted an eclectic way to maintain both relatively high recall (the proportion of nonisolated nodes) and relatively high precision (the modularity Q of the intra-core clustering).


Moreover while ending the discussion on a certain topic, there can be numerous new attempts to introduce a new topic before it becomes the focus of the dialogue.

the task of automatic topic segmentation of meeting recordings is more difficult and requires a more refined analysis.


Thus, instead of using the words as basic units for each block of text representation, we employ a "topic" basis, assuming that a few topics will capture more information than the entire huge amount of words in the vocabulary.

the m-th block of text is represented by the vector (P (z 1 |b m ), P (z 2 |b m ), ..., P (z k |b m )).


Moreover LDA leaves flexibility to assign a different topic to every observed word and a different proportion of topics for every block of text.

the joint distribution of a block of text b m and the latent variables of the model z m , θ m , Φ, given the hyperparameters α, β is further specified by: the likelihood of a block b m is derived as the marginal distribution obtained by summing over the z m,n and integrating out the distributions θ m and Φ.


Loosely speaking, a dictionary's coverage of the training set is linked to the degree of reliance placed by the CRF model on the corresponding dictionary features.

the dictionary should be made more balanced in order to avoid the potential problem of overfitting.


Ideally, we would like each bin to have equal expected number of values rather than following empirical distribution.

we attempt to discretize according to their underlying Zipfian distribution.


Adamic & Huberman (2002) shows that Zipf's law is equivalent to the power law, which describes Zipf's law in a unranked form: where X is the random variable denoting the word frequency and f X (x) is its probability density function.

approximated by integration, the expected number of values in the bin [a, b] can be calculated as each bin has equal number of values within it if and only if b/a is a constant, which is in a log scale.


We then discretize the p-value into several bins, each bin is defined by two significance levels 2 −(q+1) and 2 −q .

our AM feature is defined as: Since we have a constraint 0 ≤ q ≤ 10, thus, the last interval is [0, 2 −10 ].


The second transitive descendant is a list that we do not want to be in the result (with higher ord).


A related problem is that of support for encodings, as many of these less computerized languages do not have one standard encoding that is used by all.

there is a need of a simple and easily customizable method of adding support for a new language or encoding.


Even though with the increasing use of Unicode based encodings, the problem has reduced for many languages, we still require a facility that can allow convenient use of new languages which are not covered in Unicode.

what we need is a more customizable language-encoding support where it is very easy for the user or the developer to add support for some language-encoding.


Therefore, steps were taken to integrate Sinhala voice into MS-SAPI.

the MS-SAPI compliant Sinhala voice is accessible via any speech enabled Windows application.


Specially, the TTS system is intended to be used with screen reader software by visually impaired people.

intelligibility is a more important feature than the naturalness.


Empirical estimation of least number of input classes needed for training a neural net for Sinhala character recognition suggested about 400 classes (Weerasinghe et al., 2006).

less-complicated Knearest neighbor algorithm (KNN) was employed for the purpose of Sinhala character recognition.


The speech recognition and probability theory fields showed an upward trend of influence in the mid-1990s, and even though it has decreased in recent years, they still have a lot of influence on recent papers mainly due to techniques like expectation maximization and hidden Markov models.

our results show that overall they have been the most influential fields in the last two decades.


For example, the dependency tree in Figure 1 matches the FOCUS pattern [work → (preposition on)] and the TECHNIQUE pattern [using → (direct-object)].

the system labels the phrase corresponding to the phrase-tree headed by 'extracting', which is 'extracting information using dependency graphs', with the category FO-CUS, and similarly labels the phrase 'dependency graphs' as TECHNIQUE.


It got relatively high scores when using just the seed patterns and the titles, and hence learning new patterns reduced the precision without any significant improvement in recall.

we learned new patterns only for the TECHNIQUE and DOMAIN categories.


Particularly, among the four styles of Stanford representations, "collapsed dependency" can much simplify patterns in relation extraction since dependencies involving preposition, conjunct as well as referent of relative clause are effectively collapsed to reflect direct dependencies between content words.

the collapsed variant of Stanford scheme is adopted in this paper to refine the constituent parse tree as described in the next subsection.


A non-terminal node will not be accessed unless it is an argument of some predicate node(s).

this algorithm runs in a time that is approximately linear to the number of leaf nodes in the tree, i.e., the number of words in the source sentence.


As the number of syntactic features decreases, more rules can be unified together.

the number of pre-ordering rules and reordering rules, as shown in   Table 7 shows the final translation accuracies under BLEU score (Papineni et al., 2002) and RIBES 12 , i.e., the software implementation of Normalized Kendall's τ as proposed by (Isozaki et al., 2010a) to automatically evaluate the translation between distant language pairs based on rank correlation coefficients and significantly penalizes word order mistakes.


(p < 0.05) appeared between PAS-a and PAS-d.

we argue that the factored syntactic features such as WORD, PRED, and CAT are more essential for sub-categorizing pre-ordering rules than the remaining syntactic features.


Rather than classifying each phrase pair with either monotonous (M ), swap (S) or discontinuous (D), a probability distribution for the orientations is calculated.

for the previous word, given a weighted alignment matrix W , the phrase pair between the indexes i and j in S, S j i , and n and m in T , T m n , the probability values for each orientation are given by: The problem with the word-based lexicalized reordering model is that it is assumed that the adjacent words are translated by themselves, which is not always true in phrase-based SMT.


The translation for "很 (hen)" by itself to "sore" is not very probable, since "很 (hen)" without context is generally translated to "very", "much" or "quite".

it is more probable during decoding that the segmentation "很 疼 (hen teng)" is used.


In this case, the phrase ending with the target word "wrap" can not be extracted, since it is aligned with "把 (ba)" and "包 (bao)".

consistent phrase pairs that can be extracted must at least contain these 2 words to have "wrap" in the target.


Word-based (word-based), phrasebased (phrase-based), and phrase-based using reordering graphs (graph-based) reordering models are generally used with the regular phrase extraction algorithm, with alignment constraints defined in (Koehn et al., 2003).

we compare our method with the methods above in this environment.


where L(M ) is a function of the number of model parameters (≈ the product of the number of topics and the vocabulary size) and L(D|M ) is the negative log likelihood of the data given the model.

the goal is to jointly minimize the complexity of the model and maximize the likelihood of the data given the model, and the objective can be rewritten as: Figure 2: Two possible partitioning trees for the Amazon.com Books category tree (Figure 1).


2010  In our evaluation of automatic reference resolution, we focus on investigating the interaction between linguistic and extra-linguistic clues including eye fixations on multi-modal reference resolution.

corpora where participants frequently utter both anaphoric and exophoric referring expressions are preferable for our evaluation.


when one utters a referring expression, he potentially focuses on the object involved by fixating his eyes on it.

we use the eye fixations as clues for identifying the pieces focused on using the following criteria: the nearest piece to the eye fixation point is more likely a target of focus over all other pieces.


Elitism has been implemented at each generation by preserving the best string seen upto that generation in a location outside the population.

on termination, this location contains the best feature combination.


Initial seeds and population are chosen randomly.

for each data set we executed the proposed single and multi objective based approaches 3 times.


Moreover, the results show that the technique based on SOO (i.e., conventional GA-based method) with different objective functions provides different evaluation figures.

it is meaningful to optimize each objective function separately.


In this paper, we are exploiting a brand new type of coreference phenomenon with merely previous attempts.

we employed the much matured two-step framework with innovative extensions to accommodate complicated event coreference phenomena.


The mentions are only joined through locally selected "best-links".


Conventionally, pronouns can only appear as anaphor but not antecedent.

we do not train Pronoun-Pronoun resolvers.


Our investigation shows it actually misguided the graph partitioning methods.

we propose a revised training instance selection strategy which reflects the true sample space of the original coreferent/non-coreferent status between mentions.


B 3 provides an overall evaluation of coreference chains instead of coreferent links.


But when we look at the coreferent link results, the revised instance selection scheme improves the performance by 2.84% f-measure.

our revised instance selection scheme trains better classifier with higher coreferent link prediction results.


It was observed that all the root verb clusters had the same variations of verb phrases and hence all belonged to the same class.

from the source (English) side, only one verb could be picked up to cover all the variations, which could be then replicated for all the others verbs.


However, note that the verb-pair list used to generate the verb phrases also changes with the change in corpus size, since decreasing the corpus size would decrease the quality of the overall alignments and the number of verbs seen.

while the verb-pair list using 160k sentences had a total of 2499 verb pairs, the 20k corpus produced only 1347 verb pairs.


Adding examples will provide context information which may bias the results of the evaluation.

we added only the two missing 1-to-1 transliteration units to the list of transliteration pairs.


Inspired by example-based machine translation model (Nagao, 1984;Sato and Nagao, 1990), we assume that certain mappings in the training set are identical with the transformation of the input NE.

thus we materialize the semantic information as a set of C2E mappings coming from the training set the semantic-specific translation model incorporates semantic information by finding out the most likely mappings coming from the training set to capture the semantic structure.


If the mappings are known, the translation is achieved.

the semantic-specific model is further derived as: sc .


From Figure 1, we could find that when n=3, the proposed model gets the best performance (MRR value=0.498).


A sentence written by JSL learners might have two or more revision sentences in Lang-8 by different voluntary reviewers 10 .

the total number of corrected sentences amounts to 1,288,934.


In an actual correction, it is expected that annotators do not completely rewrite the original sentence and most character strings remain the same as the original sentence.

we investigated the quantitative distribution of Lang-8 data by breaking down the sentences according to the edit distance between the original and corrected sentences (number of deletion / number of insertion of characters in revision log).


tance between the learner's sentence and corrected sentence is 5, we simply drop the sentence for the corpus, and (3) if the corrected sentence ends with "GOOD" or "OK", we remove it and retain the sentence pair.

we obtained a corpus of 849,894 corrected and aligned sentence pairs by JSL learners.


Number of characters in the corrected sentence is six and that in the system is four, so N CORRECT = 6 and N SY ST EM = 4.


These m × k similarity values are then used as the meta features, x ′ , and we compute them for training and testing instances.

as a result of this step each instance x is now represented by m tuples of vectors, the first level feature vectors x i 1 , ..., x i |x i | and the newly generated meta feature vectors In our problem of AA, we consider four types of first level features: stylistic features, lexical features, syntactic features, and perplexity values from character 3-gram language models.


To summarize, our MSMF approach is different from previous machine learning approaches to AA in that it has an intermediate step where we generate meta features from clustering the training instances per modality.

all the vectors x sty in the training data are input to a k-means clustering algorithm.


For the stylistic modality we crafted a list of features tuned for written interactions in social networks.

we use percentages of non-alphanumeric characters that are commonly used in emoticons.


Because our focus is on evaluating the use of metafeatures for the problem of AA in web forum posts, we need to control potential confounding characteristics in the data.

for our evaluation we downloaded posts from a single topic and generated 5 data sets with a different number   Table 2 shows some statistics on these data sets.


The goal of the REF task is to return homepages representing entities to a query, and homepages sometimes contain valuable information for ranking.

it is easy and necessary to incorporate homepages of entities in ranking.


michael-schumacher.de/) do not contain any valuable information but intend to greet visitors and provide information about the site or its owner.

we retrieve text snippets hs e i from a homepage site using query Q to build a back-off model for p(h e i |Q) built from the homepage (the opening or main page of the homepage site).


Given a sentence, the task of dependency parsing is to identify the head word and its corresponding dependents and to classify their functional relationships according to a set of dependency relations (e.g., subject, modifier).

a dependency tree of a sentence encodes the dependency relation between the head words and their dependents.


Let AV G The intuition behind this approach is that the top 2 most confident labels are likely to be more informative than other labels.

we only select the two most likely labels to calculate uncertainty.


In other word, the combination was done step by step not in parallel as the other combination.

the combination of INF4 and CDT accounts for informativity prior to representativeness; this may be why ID is more successful.


Our goal is to apply compressed dependency tree to extract explicit relation between predicate and argument as precise as possible for SRL purpose.

we construct the compressed tree by identifying predicate-argument units and then re-linking them if there exist dependency relation among them.


Doing without a supertagger and using every possible supertag in a tag dictionary is computationally infeasible; most words have at least two or three possible tags; the most ambiguous word has 133 supertags (as).

we have no choice but to investigate ways to get supertags that do not rely on goldstandard syntactic annotation.


" is the nominalized form of the verb " (relegate)."

we need to calculate similarity with " " instead of " ".


When training with the full feature set, the suffix1=国 predicate is active for all of those country names and has a large feature weight associated with the preferred latent tag.

the distribution of latent tags for the rare names is skewed more toward the preferred latent tag due to strong evidence from that suffix feature.


From the figure, it is seen that the main errors are nominal and verbal categories.

we may suspect that whether the complex and frequent categories like NN and VV should be split heavily while barely split rare or simple ones.


A fundamental insight on which our work is based is that if we build an entity linking test collection using the English half of a parallel text collection, we can make use of readily available annotators and tools developed specifically for English, then project the English results onto the other language.

we apply English NER to find person names in text (Ratinov and Roth, 2009), our English entity linking system to identify candidate entity IDs, and English annotators on Amazon's Mechanical Turk to select the correct  kbid for each name.


An Example of Chinese NE Candidates Generation include 1-1, 1-2 and 1-3.

we can find the boundaries (from 1 to 3) of the corresponding Chinese translation 国家 篮球 协会.


The best F1-score of the generated training data in Table 4 is 82.11%.

we roughly infer that about 8.07% (= 1 -82.11% / 89.32%) correct NE information is lost in the process of the Chinese NER training data generation.


Although not evident from our experiments, we nevertheless assume that query synonyms are likely occurrences in event-relevant sentences, as are sub-events which entail larger events and vice versa.

a consideration of semantic and ontological features would be a logical next step for further investigation of this task and may offer interesting new views through which co-training can be utilized.